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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:601d10a3-8600-4c7b-a3d4-bd9eff84dafb.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mxnet import init, nd\n",
    "from mxnet.gluon import nn\n",
    "\n",
    "net = nn.Sequential()\n",
    "net.add(nn.Dense(256, activation='relu'))\n",
    "net.add(nn.Dense(10))\n",
    "net.initialize() # 使用默认初始化方式\n",
    "\n",
    "X = nd.random.uniform(shape=(2, 20))\n",
    "Y = net(X) # 前向计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dense0_ (\n",
       "   Parameter dense0_weight (shape=(256, 20), dtype=float32)\n",
       "   Parameter dense0_bias (shape=(256,), dtype=float32)\n",
       " ),\n",
       " mxnet.gluon.parameter.ParameterDict)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "访问模型参数\n",
    "'''\n",
    "net[0].params, type(net[0].params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dense1_ (\n",
       "   Parameter dense1_weight (shape=(10, 256), dtype=float32)\n",
       "   Parameter dense1_bias (shape=(10,), dtype=float32)\n",
       " ),\n",
       " mxnet.gluon.parameter.ParameterDict)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net[1].params, type(net[1].params)"
   ]
  },
  {
   "attachments": {
    "a4ffb8f2-61cd-421a-818d-f14827cd76ef.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:a4ffb8f2-61cd-421a-818d-f14827cd76ef.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Parameter dense0_weight (shape=(256, 20), dtype=float32),\n",
       " Parameter dense0_weight (shape=(256, 20), dtype=float32))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "后者的可读性更好\n",
    "'''\n",
    "net[0].params['dense0_weight'], net[0].weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[[ 0.06700657 -0.00369488  0.0418822  ... -0.05517294 -0.01194733\n",
       "  -0.00369594]\n",
       " [-0.03296221 -0.04391347  0.03839272 ...  0.05636378  0.02545484\n",
       "  -0.007007  ]\n",
       " [-0.0196689   0.01582889 -0.00881553 ...  0.01509629 -0.01908049\n",
       "  -0.02449339]\n",
       " ...\n",
       " [ 0.00010955  0.0439323  -0.04911506 ...  0.06975312  0.0449558\n",
       "  -0.03283203]\n",
       " [ 0.04106557  0.05671307 -0.00066976 ...  0.06387014 -0.01292654\n",
       "   0.00974177]\n",
       " [ 0.00297424 -0.0281784  -0.06881659 ... -0.04047417  0.00457048\n",
       "   0.05696651]]\n",
       "<NDArray 256x20 @cpu(0)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net[0].weight.data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[[0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " ...\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]]\n",
       "<NDArray 256x20 @cpu(0)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "权重梯度的形状和权重的形状⼀样,因为我们还没有进⾏反向传播计算，所以梯度的值全为0。\n",
    "'''\n",
    "net[0].weight.grad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
       "<NDArray 10 @cpu(0)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "访问其他层的参数，如输出层的偏差值\n",
    "（全部为 0，也是因为还没有进行反向传播计算，所以还是初始化时的数值）\n",
    "'''\n",
    "net[1].bias.data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sequential0_ (\n",
       "  Parameter dense0_weight (shape=(256, 20), dtype=float32)\n",
       "  Parameter dense0_bias (shape=(256,), dtype=float32)\n",
       "  Parameter dense1_weight (shape=(10, 256), dtype=float32)\n",
       "  Parameter dense1_bias (shape=(10,), dtype=float32)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.collect_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sequential0_ (\n",
       "  Parameter dense0_weight (shape=(256, 20), dtype=float32)\n",
       "  Parameter dense1_weight (shape=(10, 256), dtype=float32)\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "通过正则表达式来匹配参数名，从而筛选需要的参数。\n",
    "'''\n",
    "net.collect_params('.*weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[ 0.00195949 -0.0173764   0.00047347  0.00145809  0.00326049  0.00457878\n",
       " -0.00894258  0.00493839 -0.00904343 -0.01214079  0.02156406  0.01093822\n",
       "  0.01827143 -0.0104467   0.01006219  0.0051742  -0.00806932  0.01376901\n",
       "  0.00205885  0.00994352]\n",
       "<NDArray 20 @cpu(0)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "初始化模型参数\n",
    "'''\n",
    "# 非首次对模型初始化需要指定 force_reinit 为真\n",
    "net.initialize(init=init.Normal(sigma=0.01), force_reinit=True)\n",
    "net[0].weight.data()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
       "<NDArray 20 @cpu(0)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "使⽤常数来初始化权重参数。\n",
    "'''\n",
    "net.initialize(init=init.Constant(1), force_reinit=True)\n",
    "net[0].weight.data()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init dense0_weight (256, 20)\n",
      "Init dense1_weight (10, 256)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "[ 0.        -0.        -7.358638  -6.5018225  0.         0.\n",
       " -0.        -0.         0.         6.808571  -6.334403  -5.9026847\n",
       " -7.1030445 -6.700823  -0.        -7.503339  -0.         0.\n",
       "  8.80864   -9.390941 ]\n",
       "<NDArray 20 @cpu(0)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "自定义初始化方法\n",
    "令权重有⼀半概率初始化为0，有另⼀半概率初始化为[−10; −5]和[5; 10]两个区间⾥均匀分布的随机数。\n",
    "'''\n",
    "class MyInit(init.Initializer):\n",
    "    def _init_weight(self, name, data):\n",
    "        print('Init', name, data.shape)\n",
    "        data[:] = nd.random.uniform(low=-10, high=10, shape=data.shape)\n",
    "        data *= data.abs() >= 5\n",
    "        \n",
    "net.initialize(MyInit(), force_reinit=True)\n",
    "net[0].weight.data()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[ 1.         1.        -6.358638  -5.5018225  1.         1.\n",
       "  1.         1.         1.         7.808571  -5.334403  -4.9026847\n",
       " -6.1030445 -5.700823   1.        -6.503339   1.         1.\n",
       "  9.80864   -8.390941 ]\n",
       "<NDArray 20 @cpu(0)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "将隐藏层参数在现有的基础上加1。\n",
    "'''\n",
    "net[0].weight.set_data(net[0].weight.data() + 1)\n",
    "net[0].weight.data()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequential1_ (\n",
      "  Parameter dense3_weight (shape=(8, 20), dtype=float32)\n",
      "  Parameter dense3_bias (shape=(8,), dtype=float32)\n",
      "  Parameter dense2_weight (shape=(8, 8), dtype=float32)\n",
      "  Parameter dense2_bias (shape=(8,), dtype=float32)\n",
      "  Parameter dense5_weight (shape=(10, 8), dtype=float32)\n",
      "  Parameter dense5_bias (shape=(10,), dtype=float32)\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "[1. 1. 1. 1. 1. 1. 1. 1.]\n",
       "<NDArray 8 @cpu(0)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "共享模型参数\n",
    "'''\n",
    "net = nn.Sequential()\n",
    "shared = nn.Dense(8, activation='relu')\n",
    "net.add(nn.Dense(8, activation='relu'),\n",
    "       shared,\n",
    "       nn.Dense(8, activation='relu', params=shared.params),\n",
    "       nn.Dense(10))\n",
    "net.initialize()\n",
    "\n",
    "X = nd.random.uniform(shape=(2, 20))\n",
    "net(X)\n",
    "\n",
    "print(net.collect_params())\n",
    "net[1].weight.data()[0] == net[2].weight.data()[0]"
   ]
  },
  {
   "attachments": {
    "562914d0-d2b7-4f2e-aec7-47cba999df55.png": {
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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 练习\n",
    "\n",
    "#### 1.尝试在net.initialize()后、 net(X)前访问模型参数，观察模型参数的形状。\n",
    "\n",
    "答：![image.png](attachment:562914d0-d2b7-4f2e-aec7-47cba999df55.png)\n",
    "\n",
    "在初始化时模型形状如图所示，这是由于mxnet在模型构造时会自适应所输出进来数据维度来对模型的每一层构造进行调整\n",
    "\n",
    "![image.png](attachment:dc9ae1de-1f54-40eb-aaa2-e739ee1c9f58.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.构造⼀个含共享参数层的多层感知机并训练。在训练过程中，观察每⼀层的模型参数和梯度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyInit(init.Initializer):\n",
    "    def __init__(self):\n",
    "        super(MyInit, self).__init__()\n",
    "        self._verbose = True\n",
    "\n",
    "    def _init_weight(self, _, arr):\n",
    "        # 初始化权重，使用out=arr后我们不需指定形状\n",
    "        print('init weight', arr.shape)\n",
    "        nd.random.uniform(low=5, high=10, out=arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = nn.Sequential()\n",
    "with net.name_scope():\n",
    "    net.add(nn.Dense(4, in_units=4, activation=\"relu\"))\n",
    "    net.add(nn.Dense(4, in_units=4, activation=\"relu\", params=net[-1].params))\n",
    "    net.add(nn.Dense(1, in_units=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init weight (4, 4)\n",
      "init weight (1, 4)\n",
      "\n",
      "[[8.637718  6.5009475 5.1213636 6.3339405]\n",
      " [7.15058   5.997648  8.260623  7.4631624]\n",
      " [9.26623   6.725689  7.376624  6.6530027]\n",
      " [9.846029  5.331036  6.3281627 5.0693254]]\n",
      "<NDArray 4x4 @cpu(0)>\n",
      "\n",
      "[[8.637718  6.5009475 5.1213636 6.3339405]\n",
      " [7.15058   5.997648  8.260623  7.4631624]\n",
      " [9.26623   6.725689  7.376624  6.6530027]\n",
      " [9.846029  5.331036  6.3281627 5.0693254]]\n",
      "<NDArray 4x4 @cpu(0)>\n"
     ]
    }
   ],
   "source": [
    "net.initialize(MyInit())\n",
    "print(net[0].weight.data())\n",
    "print(net[1].weight.data())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mxnet import gluon\n",
    "from mxnet import autograd\n",
    "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})\n",
    "a = nd.random_normal(shape=(4, 4))\n",
    "with autograd.record():\n",
    "    y = net(a)\n",
    "y.backward()\n",
    "trainer.step(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[[275.98456  822.29974   24.22686  400.1766  ]\n",
      " [360.07507  733.76746  182.95319  420.94806 ]\n",
      " [315.61124  753.53674  111.095184 401.89697 ]\n",
      " [297.39737  701.0559   108.69843  376.02023 ]]\n",
      "<NDArray 4x4 @cpu(0)>\n",
      "\n",
      "[[275.98456  822.29974   24.22686  400.1766  ]\n",
      " [360.07507  733.76746  182.95319  420.94806 ]\n",
      " [315.61124  753.53674  111.095184 401.89697 ]\n",
      " [297.39737  701.0559   108.69843  376.02023 ]]\n",
      "<NDArray 4x4 @cpu(0)>\n"
     ]
    }
   ],
   "source": [
    "print(net[0].weight.grad())\n",
    "print(net[1].weight.grad())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[[-18.960737  -75.72903     2.6986775 -33.683723 ]\n",
      " [-28.856928  -67.3791    -10.034696  -34.631645 ]\n",
      " [-22.294895  -68.62799    -3.732895  -33.536694 ]\n",
      " [-19.893707  -64.77455    -4.541681  -32.5327   ]]\n",
      "<NDArray 4x4 @cpu(0)>\n",
      "\n",
      "[[-18.960737  -75.72903     2.6986775 -33.683723 ]\n",
      " [-28.856928  -67.3791    -10.034696  -34.631645 ]\n",
      " [-22.294895  -68.62799    -3.732895  -33.536694 ]\n",
      " [-19.893707  -64.77455    -4.541681  -32.5327   ]]\n",
      "<NDArray 4x4 @cpu(0)>\n"
     ]
    }
   ],
   "source": [
    "print(net[0].weight.data())\n",
    "print(net[1].weight.data())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sequential4_ (\n",
       "  Parameter sequential4_dense0_weight (shape=(4, 4), dtype=float32)\n",
       "  Parameter sequential4_dense0_bias (shape=(4,), dtype=float32)\n",
       "  Parameter sequential4_dense2_weight (shape=(1, 4), dtype=float32)\n",
       "  Parameter sequential4_dense2_bias (shape=(1,), dtype=float32)\n",
       ")"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.collect_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "延后初始化\n",
    "'''\n",
    "from mxnet import init, nd\n",
    "from mxnet.gluon import nn\n",
    "\n",
    "class MyInit(init.Initializer):\n",
    "    def _init_weight(self, name, data):\n",
    "        print('Init', name, data.shape)\n",
    "        # 实际的初始化逻辑在此省略了\n",
    "        \n",
    "net = nn.Sequential()\n",
    "net.add(nn.Dense(256, activation='relu'),\n",
    "        nn.Dense(10))\n",
    "\n",
    "net.initialize(init=MyInit())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init dense15_weight (256, 20)\n",
      "Init dense16_weight (10, 256)\n"
     ]
    }
   ],
   "source": [
    "X = nd.random.uniform(shape=(2, 20))\n",
    "Y = net(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "这个初始化只会在第⼀次前向计算时被调⽤。之后我们再运⾏前向计算net(X)时则不会\n",
    "重新初始化，因此不会再次产⽣MyInit实例的输出。\n",
    "'''\n",
    "Y = net(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init dense15_weight (256, 20)\n",
      "Init dense16_weight (10, 256)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "避免延后初始化\n",
    "第⼀种情况是我们要对已初始化的模型重新初始化时。因为参数形状不会发⽣变化，所以系统能\n",
    "够⽴即进⾏重新初始化\n",
    "'''\n",
    "net.initialize(init=MyInit(), force_reinit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init dense17_weight (256, 20)\n",
      "Init dense18_weight (10, 256)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "第⼆种情况是我们在创建层的时候指定了它的输⼊个数，使系统不需要额外的信息来推测参数形状。\n",
    "下例中我们通过 in_units 来指定每个全连接层的输⼊个数，\n",
    "使初始化能够在 initialize 函数被调⽤时⽴即发⽣。\n",
    "'''\n",
    "net = nn.Sequential()\n",
    "net.add(nn.Dense(256, in_units=20, activation='relu'))\n",
    "net.add(nn.Dense(10, in_units=256))\n",
    "\n",
    "net.initialize(init=MyInit())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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